US10984564B2 - Image noise estimation using alternating negation - Google Patents

Image noise estimation using alternating negation Download PDF

Info

Publication number
US10984564B2
US10984564B2 US16/463,859 US201716463859A US10984564B2 US 10984564 B2 US10984564 B2 US 10984564B2 US 201716463859 A US201716463859 A US 201716463859A US 10984564 B2 US10984564 B2 US 10984564B2
Authority
US
United States
Prior art keywords
projection data
image
noise
views
generate
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active, expires
Application number
US16/463,859
Other languages
English (en)
Other versions
US20190385345A1 (en
Inventor
Frank Bergner
Bernhard Johannes Brendel
Thomas Koehler
Kevin Martin Brown
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Koninklijke Philips NV
Original Assignee
Koninklijke Philips NV
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Koninklijke Philips NV filed Critical Koninklijke Philips NV
Priority to US16/463,859 priority Critical patent/US10984564B2/en
Assigned to KONINKLIJKE PHILIPS N.V. reassignment KONINKLIJKE PHILIPS N.V. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: BROWN, Kevin Martin, BERGNER, FRANK, BRENDEL, BERNHARD JOHANNES, KOEHLER, THOMAS
Publication of US20190385345A1 publication Critical patent/US20190385345A1/en
Application granted granted Critical
Publication of US10984564B2 publication Critical patent/US10984564B2/en
Active legal-status Critical Current
Adjusted expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/00—2D [Two Dimensional] image generation
    • G06T11/003—Reconstruction from projections, e.g. tomography
    • G06T11/006—Inverse problem, transformation from projection-space into object-space, e.g. transform methods, back-projection, algebraic methods
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/00—2D [Two Dimensional] image generation
    • G06T11/003—Reconstruction from projections, e.g. tomography
    • G06T11/005—Specific pre-processing for tomographic reconstruction, e.g. calibration, source positioning, rebinning, scatter correction, retrospective gating
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N23/00—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00
    • G01N23/02—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by transmitting the radiation through the material
    • G01N23/04—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by transmitting the radiation through the material and forming images of the material
    • G01N23/046—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by transmitting the radiation through the material and forming images of the material using tomography, e.g. computed tomography [CT]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/70—Denoising; Smoothing
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2223/00—Investigating materials by wave or particle radiation
    • G01N2223/40—Imaging
    • G01N2223/401—Imaging image processing
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2223/00—Investigating materials by wave or particle radiation
    • G01N2223/40—Imaging
    • G01N2223/419—Imaging computed tomograph
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10081—Computed x-ray tomography [CT]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2210/00—Indexing scheme for image generation or computer graphics
    • G06T2210/41—Medical

Definitions

  • the following generally relates to estimating image noise for de-noising volumetric image data and more particularly to estimating image noise using an alternating negation approach, and is described with particular application to X-ray imaging such as computed tomography (CT) and/or other X-ray imaging.
  • CT computed tomography
  • a CT scanner includes an x-ray tube mounted on a rotatable gantry that rotates around an examination region about a z-axis.
  • a detector array subtends an angular arc opposite the examination region from the x-ray tube.
  • the x-ray tube emits radiation that traverses the examination region and a portion of an object and/or subject therein.
  • the detector array detects radiation that traverses the examination region and generates projection data indicative thereof.
  • a reconstructor reconstructs the projection data, which produces volumetric (three-dimensional) image data indicative of the examination region and the portion of the object and/or subject therein. The following describes projection data as utilized herein.
  • a focal spot 102 represents a region of the radiation source from which x-ray radiation 104 is emitted.
  • a beam shaping device such as a collimator 106 , defines a beam 108 (e.g., a fan beam in the illustrated example) which traverses an examination region 110 .
  • a detector array 112 is disposed opposite the focal spot 102 , across the examination region 110 .
  • the detector array 112 includes at least one row of detector elements 114 1 , . . . , 114 N ( 114 ).
  • Each detector element 114 detects x-ray radiation from the fan beam impinging thereon and generates an electrical signal indicative of a total X-ray attenuation along a path from the focal spot 102 to the detector element 114 (a line integral).
  • FIG. 1 shows the focal spot 102 , the fan beam 108 , and the detector array 112 at one angle 113 (e.g., ⁇ 1 ) on the path 116
  • FIG. 2 shows them at a different angle 115 (e.g., ⁇ N ) on the path 116 .
  • the set of lines integral for the detector elements 114 at any one angle is referred to herein as a view.
  • the set of lines integrals at the angle 113 represents one view, and the set of lines integrals at the angle 115 represents another view.
  • the set of views for a scan is referred to herein as projection data.
  • Denoising algorithms include algorithms that denoise in the projection domain (line integrals) and/or in the image domain (volumetric image data).
  • a noise image which provides a map of the input image noise level in each pixel, is used to adjust the denoising strength for spatially varying noise levels.
  • U.S. Pat. No. 7,706,497 B2 describes a method for determining a noise image for image domain denoising. This method includes splitting the projection data into odd views (e.g., ⁇ 1 , ⁇ 3 , ⁇ 5 , . . . ) and even views (e.g., ⁇ 2 , ⁇ 4 , ⁇ 6 , . . .
  • the set of odd views is reconstructed via one processing chain to produce an odd view image and the even set of view is reconstructed via a different processing chain to produce an even view image.
  • the odd and even view images are subtracted to create the noise image.
  • the idea is that the two sets of views are from a scan of a same structure. As such, both images essentially represent the same structure, and subtracting the two images will cancel out the structure, leaving only uncorrelated noise.
  • FIG. 3 illustrates this process via a flow diagram.
  • Projection data generated from a scan is received at 302 , and is split into odds views and even views at 304 .
  • An odd view processing chain 306 and 308 rebins the odd views from fan beam geometry to parallel beam geometry and reconstructs the rebinned odd views, producing an odd view image.
  • An even view processing chain 310 and 312 rebins the even views from fan beam geometry to parallel beam geometry and reconstructs the rebinned even views, producing an even view image.
  • the odd and even view images are subtracted, producing noise image.
  • a processing chain 316 and 318 rebins all of views from fan beam geometry to parallel beam geometry and reconstructs the rebinned views, producing a structural image.
  • local noise variance estimations are estimated from the noise image to produce a noise map for the structural image, and the structural image is de-noised using the noise map.
  • an imaging system includes a radiation source configured to emit X-ray radiation, a detector array configured to detect X-ray radiation and generate projection data indicative thereof, wherein the projection data includes a plurality of views, and a first processing chain configured to reconstruct the projection data and generate a noise only image.
  • a method in another aspect, includes receiving projection data produced by an imaging system and processing the projection data with a first processing chain configured to reconstruct the projection data and generate a noise only image.
  • a computer readable storage medium is encoded with computer readable instructions.
  • the computer readable instructions when executed by a processer, causes the processor to: scan an object or subject with an x-ray imaging system and generate projection data, process the projection data with a first processing chain configured to reconstruct the projection data and generate a noise only image, process the projection data with a second processing chain configured to reconstruct the projection data and generate a structure image, and de-noise the structure image based on the noise only image.
  • the invention may take form in various components and arrangements of components, and in various steps and arrangements of steps.
  • the drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention.
  • FIG. 1 describes one of a plurality of views of projection data.
  • FIG. 2 describes a different view of the plurality of views.
  • FIG. 3 is a flow diagram of a prior art method for producing a noise image for image domain denoising by splitting the views into odd and even views.
  • FIG. 4 schematically illustrates an example imaging system with a processing chain, which includes a projection data processor, for generating an only noise image.
  • FIG. 5 schematically illustrates an example of the projection data processor.
  • FIG. 6 illustrates an example method in accordance with an embodiment herein.
  • FIG. 7 shows a comparison of a noise image generated with the approach described herein and a noise image generated with a prior art approach.
  • FIG. 4 illustrates an imaging system 400 such as a computed tomography (CT) scanner.
  • the imaging system 400 includes a generally stationary gantry 402 and a rotating gantry 404 .
  • the rotating gantry 404 is rotatably supported by the stationary gantry 402 via a bearing or the like and rotates around an examination region 406 about a longitudinal or z-axis.
  • a radiation source 408 such as an x-ray tube, is rotatably supported by the rotating gantry 404 .
  • the radiation source 408 rotates with the rotating gantry 404 and emits radiation that traverses the examination region 406 .
  • a source collimator includes collimation members that collimate the radiation to form a generally fan shaped radiation beam. In other embodiments, the beam is shape to form a generally cone, wedge or otherwise shaped radiation beam.
  • a one or two-dimensional radiation sensitive detector array 410 subtends an angular arc opposite the radiation source 408 across the examination region 406 .
  • the detector array 410 includes one or more rows of detectors that extend along the z-axis direction.
  • the detector array 410 detects radiation traversing the examination region 406 and generates projection data indicative thereof.
  • the projection data includes a plurality of views, each view corresponding to different angle of rotation at which data is acquired as the radiation source 408 and the detector array 410 around the examination region 406 .
  • Each view includes a plurality of values (line integrals) output by the detectors of the detector array 410 , and each value is indicative of X-ray attenuation along a path from the radiation source 408 to a detector of the detector array 410 producing that value.
  • a first (conventional) processing chain 412 includes a rebinning processor 414 and a reconstruction processor 416 .
  • a second processing chain 418 includes a projection data processor 420 , the rebinning processor 414 and the reconstruction processor 416 .
  • the processing chains 412 and 418 share a rebinning processor and a reconstruction processor.
  • each of the processing chains 412 and 418 has its own rebinning processor and/or a reconstruction processor.
  • the rebinning processor 414 rebins the views from fan beam geometry to parallel beam geometry.
  • the reconstruction processor 416 reconstructs the rebinned views and generates volumetric image data (referred to as a “structural image”).
  • the projection data processor 420 pre-processes the views before rebinning by the rebinning processor 414 and reconstruction by the reconstruction processor 416 so that structure represented in the projection data cancels out during the rebinning and reconstruction acts, producing an only noise image.
  • a multiplier 502 multiplies the views of the projection data by a predetermined multiplication factor 504 . In one instance, this includes multiplying a first view by a value of positive one, a second view by a value of negative one, a third view by a value of positive one, a fourth view by a value of negative one, . . . an (n ⁇ 1)th view by a value of positive one, an nth view by a value of negative one, or vice versa in which the first view is multiplied by a value of negative one, a second view is multiplied by a value of negative one, . . . .
  • This approach is referred to herein as an alternating negation approach.
  • the predetermined multiplication factor 504 alternates between positive one and negative from view to view.
  • the multiplication factor 504 does not have to alternate view to view.
  • the multiplication factor 504 can instead alternate every set of two or more views. For instance, this may include multiplying a first view by a value of positive one, a second view by a value of positive one, a third view by a value of negative one, a fourth view by a value of negative one, . . . .
  • the predetermined multiplication factor 504 can include values other than positive and negative one.
  • the output of the first processing chain 412 is conventional volumetric image data with pixels/voxels representing the scan structure along with noise
  • the output of the second processing chain 418 is an only noise image.
  • the rebinning processor 414 implements a conventional or other fan-to-parallel beam rebinning algorithm
  • the reconstruction processor 416 implements a conventional or other filtered back-projection (FBP) reconstruction algorithm and/or other reconstruction such as an iterative reconstruction algorithm and/or other reconstruction algorithm.
  • FBP filtered back-projection
  • a de-noising processor 422 receives both the reconstructed image (3-D image data) and the noise image, and de-noises the reconstructed image using the noise image. For example, the de-noising processor 422 produces a noise map or estimates of a noise variance and/or a noise standard deviation from the noise image. In one instance, this is achieved by taking a local varaiance or standard deviation of a small region of interst (ROI) and move that through the image.
  • ROI interst
  • An example image domain de-noising algorithm which utilizes a noise standard deviation is described in U.S. Pat. No. 9,1591,22 B2, filed Nov. 12, 2012, and entitled “Image domain de-noising,” US 2016/0140725 A1, filed Jun. 26, 2014, and entitled, “Methods of utilizing image noise information,” and U.S. Pat. No. 8,938,110 B2, filed Oct. 29, 2015, and entitled “Enhanced image data/dose reduction,” which are incorporated by reference in their entireties herein.
  • a subject support 424 such as a couch, supports an object or subject such as a human patient in the examination region 406 .
  • the subject support 424 is configured to move the object or subject for loading, scanning, and/or unloading the object or subject.
  • a general-purpose computing system or computer serves as an operator console 426 .
  • the console 426 includes a human readable output device such as a monitor and an input device such as a keyboard, mouse, etc.
  • Software resident on the console 426 allows the operator to interact with and/or operate the imaging system 400 via a graphical user interface (GUI) or otherwise. This includes selecting an imaging protocol such as one utilizing the de-noising processor 422 to de-noise the reconstructed image.
  • GUI graphical user interface
  • the projection data can be received and/or retrieved from a data repository such as a picture archiving and communication system (PACS), a radiology information system (RIS), a hospital information system (HIS), and/or other data repository.
  • a data repository such as a picture archiving and communication system (PACS), a radiology information system (RIS), a hospital information system (HIS), and/or other data repository.
  • the reconstructed image, the noise image and/or the de-noised reconstructed image can be stored in the data repository.
  • At least one of the projection data processor 420 , the rebinning processor 414 , the reconstruction processor 416 , and the de-noising processor 422 is part of the console 426 and/or a computing device remote from and external to the imaging system 100 .
  • one or more of these components can be part of a “cloud” based service, distributed over a plurality of devices, part of another imaging system, etc.
  • FIG. 6 illustrates an example method in accordance with an embodiment herein.
  • projection data from a scan of a subject or object is obtained.
  • the projection data can be generated by the imaging system 400 and/or other imaging system during a scan and obtained therefrom and/or from a data repository.
  • the projection data is processed through a conventional processing chain which includes rebinning the projection data from fan beam to parallel beam format and reconstructing the parallel beam data via filtered back projection, producing a structural image (volumetric image data) of the scanned subject or object.
  • the projection data is also pre-processed by alternating multiplying views by positive one and negative one, or vice versa, as described herein, producing pre-processed projection data.
  • the pre-processed projection data is processed through a conventional processing chain which includes rebinning the pre-processed projection data from fan beam to parallel beam format and reconstructing the parallel beam data via filtered back projection, producing a noise only image.
  • the structural image is de-noised using the noise image, as described herein, producing a de-noised structural image.
  • the above methods may be implemented by way of computer readable instructions, encoded or embedded on computer readable storage medium, which, when executed by a computer processor(s), cause the processor(s) to carry out the described acts. Additionally or alternatively, at least one of the computer readable instructions is carried by a signal, carrier wave or other transitory medium.
  • FIG. 7 shows a structural image 702 , a noise image 704 using the approach described herein, and a noise image 706 using the prior art approach described in FIG. 3 .
  • the noise images 704 and 706 show a noise value for each pixel/voxel of the structural image 702 .
  • a noise standard deviation is computed for the pixel/voxels values of a region of interest (ROI) 708 of the structural image 702 .
  • ROI 708 is placed over an area of a same anatomical tissue that appears to have a same or similar CT number (“flat”).
  • the noise standard deviation for the ROI 708 is 73.
  • a ROI 710 is placed at the same location as the ROI 708 in the structural image 702 .
  • a ROI 712 is also placed at the same location as the ROI 708 in the structural image 702 .
  • using the approach described herein produces a noise standard deviation ( 69 ) which is close to the noise standard deviation for the ROI 708 of the structural image 702 , and closer to the noise standard deviation for the ROI 708 of the structural image 702 relative to the noise standard deviation ( 111 ) for the ROI 712 of the prior art noise image 706 .

Landscapes

  • Theoretical Computer Science (AREA)
  • Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • Biochemistry (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Pulmonology (AREA)
  • General Health & Medical Sciences (AREA)
  • Radiology & Medical Imaging (AREA)
  • Immunology (AREA)
  • Pathology (AREA)
  • Algebra (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Mathematical Physics (AREA)
  • Pure & Applied Mathematics (AREA)
  • Apparatus For Radiation Diagnosis (AREA)
US16/463,859 2016-12-06 2017-12-06 Image noise estimation using alternating negation Active 2038-01-23 US10984564B2 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
US16/463,859 US10984564B2 (en) 2016-12-06 2017-12-06 Image noise estimation using alternating negation

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
US201662430424P 2016-12-06 2016-12-06
PCT/EP2017/081593 WO2018104349A1 (en) 2016-12-06 2017-12-06 Image noise estimation using alternating negation
US16/463,859 US10984564B2 (en) 2016-12-06 2017-12-06 Image noise estimation using alternating negation

Publications (2)

Publication Number Publication Date
US20190385345A1 US20190385345A1 (en) 2019-12-19
US10984564B2 true US10984564B2 (en) 2021-04-20

Family

ID=60923439

Family Applications (1)

Application Number Title Priority Date Filing Date
US16/463,859 Active 2038-01-23 US10984564B2 (en) 2016-12-06 2017-12-06 Image noise estimation using alternating negation

Country Status (5)

Country Link
US (1) US10984564B2 (enExample)
EP (1) EP3552181B1 (enExample)
JP (1) JP7123927B2 (enExample)
CN (1) CN110073412B (enExample)
WO (1) WO2018104349A1 (enExample)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2023030922A1 (en) 2021-08-31 2023-03-09 Koninklijke Philips N.V. Denoising of medical images using a machine-learning method
US20230136930A1 (en) * 2020-04-16 2023-05-04 Hamamatsu Photonics K.K. Radiographic image processing method, trained model, radiographic image processing module, radiographic image processing program, and radiographic image processing system

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP7395523B2 (ja) * 2021-02-02 2023-12-11 富士フイルムヘルスケア株式会社 医用画像処理装置および医用画像処理方法
TWI890836B (zh) * 2021-02-15 2025-07-21 日商濱松赫德尼古斯股份有限公司 放射線圖像獲取裝置、放射線圖像獲取系統及放射線圖像獲取方法
DE102023209197A1 (de) 2023-09-21 2025-03-27 Siemens Healthineers Ag Verfahren und Vorrichtung zum Entrauschen einer Tomographie-Aufnahme

Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070140407A1 (en) 2005-10-12 2007-06-21 Siemens Corporate Research Inc Reduction of Streak Artifacts In Low Dose CT Imaging through Multi Image Compounding
US20080095462A1 (en) 2006-10-19 2008-04-24 Jiang Hsieh Methods and Apparatus for Noise Estimation
US20090232269A1 (en) * 2008-03-14 2009-09-17 Jiang Hsieh Methods and apparatus for noise estimation for multi-resolution anisotropic diffusion filtering
US20110158498A1 (en) 2009-12-30 2011-06-30 General Electric Company Noise reduction method for dual-energy imaging
US20120321157A1 (en) 2011-06-14 2012-12-20 Toshiba Medical Systems Corporation Method and system for estimating noise level
US20130089252A1 (en) * 2010-06-21 2013-04-11 Koninklijke Philips Electronics N.V. Method and system for noise reduction in low dose computed tomography
US20140219529A1 (en) 2013-02-01 2014-08-07 Toshiba Medical Systems Corporation Alterative noise map estimation methods for ct images
US20140270454A1 (en) 2013-03-12 2014-09-18 Wisconsin Alumni Research Foundation System and method for estimating a statistical noise map in x-ray imaging applications
US20160143606A1 (en) 2013-07-25 2016-05-26 Hitachi Medical Corporation X-ray ct apparatus
WO2016132880A1 (ja) 2015-02-16 2016-08-25 株式会社日立製作所 演算装置、ｘ線ｃｔ装置、及び画像再構成方法

Family Cites Families (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6078639A (en) 1997-11-26 2000-06-20 Picker International, Inc. Real time continuous CT imaging
EP1599836B1 (en) 2003-02-14 2010-04-21 Koninklijke Philips Electronics N.V. System and method for helical cone-beam computed tomography with exact reconstruction
RU2565507C2 (ru) 2009-11-25 2015-10-20 Конинклейке Филипс Электроникс Н.В. Система и способ для улучшения качества изображения
WO2013076613A1 (en) 2011-11-23 2013-05-30 Koninklijke Philips Electronics N.V. Image domain de-noising
CN103366389A (zh) * 2013-04-27 2013-10-23 中国人民解放军北京军区总医院 Ct图像重建方法
WO2014207139A1 (en) 2013-06-28 2014-12-31 Koninklijke Philips N.V. Methods of utilizing image noise information
US9357976B2 (en) * 2013-10-24 2016-06-07 General Electric Company System and method of noise deletion in computed tomography
JP6713860B2 (ja) 2016-07-04 2020-06-24 株式会社日立製作所 画像再構成装置、ｘ線ｃｔ装置、および、画像再構成方法

Patent Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070140407A1 (en) 2005-10-12 2007-06-21 Siemens Corporate Research Inc Reduction of Streak Artifacts In Low Dose CT Imaging through Multi Image Compounding
US20080095462A1 (en) 2006-10-19 2008-04-24 Jiang Hsieh Methods and Apparatus for Noise Estimation
US20090232269A1 (en) * 2008-03-14 2009-09-17 Jiang Hsieh Methods and apparatus for noise estimation for multi-resolution anisotropic diffusion filtering
US7706497B2 (en) 2008-03-14 2010-04-27 General Electric Company Methods and apparatus for noise estimation for multi-resolution anisotropic diffusion filtering
US20110158498A1 (en) 2009-12-30 2011-06-30 General Electric Company Noise reduction method for dual-energy imaging
US20130089252A1 (en) * 2010-06-21 2013-04-11 Koninklijke Philips Electronics N.V. Method and system for noise reduction in low dose computed tomography
US20120321157A1 (en) 2011-06-14 2012-12-20 Toshiba Medical Systems Corporation Method and system for estimating noise level
US20140219529A1 (en) 2013-02-01 2014-08-07 Toshiba Medical Systems Corporation Alterative noise map estimation methods for ct images
US20140270454A1 (en) 2013-03-12 2014-09-18 Wisconsin Alumni Research Foundation System and method for estimating a statistical noise map in x-ray imaging applications
US20160143606A1 (en) 2013-07-25 2016-05-26 Hitachi Medical Corporation X-ray ct apparatus
WO2016132880A1 (ja) 2015-02-16 2016-08-25 株式会社日立製作所 演算装置、ｘ線ｃｔ装置、及び画像再構成方法

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
Kim, Forward-Projection Architecture for Fast Iterative Image Reconstruction in X-Ray CT, Oct. 2012, IEEE (Year: 2012). *
PCT International Search Report, International application No. PCT/EP2017/081593, dated Apr. 11, 2018.

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20230136930A1 (en) * 2020-04-16 2023-05-04 Hamamatsu Photonics K.K. Radiographic image processing method, trained model, radiographic image processing module, radiographic image processing program, and radiographic image processing system
WO2023030922A1 (en) 2021-08-31 2023-03-09 Koninklijke Philips N.V. Denoising of medical images using a machine-learning method

Also Published As

Publication number Publication date
CN110073412B (zh) 2023-11-03
WO2018104349A1 (en) 2018-06-14
EP3552181B1 (en) 2021-11-10
US20190385345A1 (en) 2019-12-19
EP3552181A1 (en) 2019-10-16
JP7123927B2 (ja) 2022-08-23
JP2019535451A (ja) 2019-12-12
CN110073412A (zh) 2019-07-30

Similar Documents

Publication Publication Date Title
US11847761B2 (en) Medical image processing apparatus having a plurality of neural networks corresponding to different fields of view
US9959640B2 (en) Iterative image reconstruction with a sharpness driven regularization parameter
CN103649990B (zh) 用于谱ct的图像处理
US9031299B2 (en) Low dose CT denoising
JP6169558B2 (ja) コントラスト依存の解像度をもつ画像
JP5588849B2 (ja) Ｘ線画像の時間分解能を高めるシステム
US9025838B2 (en) Apparatus and method for hybrid reconstruction of an object from projection data
US10984564B2 (en) Image noise estimation using alternating negation
US9449404B2 (en) Iterative image reconstruction with regularization
EP3210192B1 (en) Start image for spectral image iterative reconstruction
EP2427868A1 (en) System and method for generating a tomographic reconstruction filter
US11238625B2 (en) Imaging system extended field-of-view
WO2019149762A1 (en) Non-spectral computed tomography (ct) scanner configured to generate spectral volumetric image data
JP2021502842A (ja) 複数の異なるタイプの投影データのボクセル毎に１回のジオメトリ演算を行う単一のｃｔ逆投影器
US12141899B2 (en) Cone beam artifact correction for gated imaging

Legal Events

Date Code Title Description
AS Assignment

Owner name: KONINKLIJKE PHILIPS N.V., NETHERLANDS

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:BERGNER, FRANK;BRENDEL, BERNHARD JOHANNES;KOEHLER, THOMAS;AND OTHERS;SIGNING DATES FROM 20170612 TO 20180212;REEL/FRAME:049274/0620

FEPP Fee payment procedure

Free format text: ENTITY STATUS SET TO UNDISCOUNTED (ORIGINAL EVENT CODE: BIG.); ENTITY STATUS OF PATENT OWNER: LARGE ENTITY

STPP Information on status: patent application and granting procedure in general

Free format text: DOCKETED NEW CASE - READY FOR EXAMINATION

STPP Information on status: patent application and granting procedure in general

Free format text: RESPONSE TO NON-FINAL OFFICE ACTION ENTERED AND FORWARDED TO EXAMINER

STPP Information on status: patent application and granting procedure in general

Free format text: NOTICE OF ALLOWANCE MAILED -- APPLICATION RECEIVED IN OFFICE OF PUBLICATIONS

STPP Information on status: patent application and granting procedure in general

Free format text: PUBLICATIONS -- ISSUE FEE PAYMENT RECEIVED

STPP Information on status: patent application and granting procedure in general

Free format text: PUBLICATIONS -- ISSUE FEE PAYMENT VERIFIED

STCF Information on status: patent grant

Free format text: PATENTED CASE

MAFP Maintenance fee payment

Free format text: PAYMENT OF MAINTENANCE FEE, 4TH YEAR, LARGE ENTITY (ORIGINAL EVENT CODE: M1551); ENTITY STATUS OF PATENT OWNER: LARGE ENTITY

Year of fee payment: 4